Intention Modulation for Multi-step Tasks in Continuous Time Active Inference

We extend an Active Inference theory in continuous time of how neural circuitry in the Dorsal Visual Stream (DVS) and the Posterior Parietal Cortex (PPC) implement visually guided goal-directed behavior with novel capacity to resolve multi-step tasks. According to the theory, the PPC maintains a high-level internal representation of the causes of the environment (belief), including bodily states and objects in the scene, and by generating sensory predictions and comparing them with observations it is able to learn and infer the causal relationships and latent states of the external world. We propose that multi-task goal-directed behavior may be achieved by decomposing the belief dynamics into a set of intention functions that independently pull the belief towards different goals; multi-step tasks could be solved by dynamically modulating these intentions within the PPC. This low-level solution in continuous time is applicable to multi-phase actions consisting of a priori defined steps as an alternative to the more general hybrid discrete-continuous approach. As a demonstration, we emulated an agent embodying an actuated upper limb and proprioceptive, visual and tactile sensory systems. Visual information was obtained with the help of a Variational Autoencoder (VAE) simulating the DVS, which allows to dynamically infer the current posture configuration through prediction error minimization and, importantly, an intended future posture corresponding to the visual targets. We assessed the approach on a task including two steps: reaching a target and returning to a home position. We show that by defining a functional that governs the activation of different intentions implementing the corresponding steps, the agent can easily solve the overall task.